Detection of human, legitimate bot, and malicious bot in online social networks based on wavelets

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBarbon, Sylvio-
Autor(es): dc.creatorCampos, Gabriel F.C.-
Autor(es): dc.creatorTavares, Gabriel M.-
Autor(es): dc.creatorIgawa, Rodrigo A.-
Autor(es): dc.creatorProença, Mario L.-
Autor(es): dc.creatorGuido, Rodrigo Capobianco-
Data de aceite: dc.date.accessioned2021-03-11T00:57:04Z-
Data de disponibilização: dc.date.available2021-03-11T00:57:04Z-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2018-02-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1145/3183506-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/179753-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/179753-
Descrição: dc.descriptionSocial interactions take place in environments that influence people's behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a waveletbased model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users' textual content.We create the feature vector from the DiscreteWavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classificationmodel using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: Single theme and miscellaneous one.-
Idioma: dc.languageen-
Relação: dc.relationACM Transactions on Multimedia Computing, Communications and Applications-
Relação: dc.relation0,408-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectBots-
Palavras-chave: dc.subjectOSN frauds-
Palavras-chave: dc.subjectText mining-
Palavras-chave: dc.subjectWavelets-
Palavras-chave: dc.subjectWriting style-
Título: dc.titleDetection of human, legitimate bot, and malicious bot in online social networks based on wavelets-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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